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에이펙스 주최.일체 포함

우리는 모빌리티 자동화 기술을 연구하는 개발자 및 애호가 커뮤니티입니다. 당사의 이벤트는 안전하고 신뢰할 수 있는 자율 주행 차량의 개발을 가속화하기 위해 지식, 경험 및 모범 사례를 공유하는 것을 목표로 합니다. 오픈 소스 Autoware 및 ROS를 중심으로 학문적 진보, 기업 및 기술의 성장하는 생태계는 연구에서 이 기술 스택의 상업적 생산에 이르는 경로를 제공합니다.

과거 사건들

Practical Memory Pool Based Allocators For Modern C++ - Misha Shalem - CppCon 2020

Practical Memory Pool Based Allocators For Modern C++ - Misha Shalem - CppCon 2020 --- Runtime-deterministic memory allocations are a crucial aspect of any safety-critical real-time system. One of the simplest and widely adopted allocation mechanisms used in such systems is a memory pool with fixed block sizes. Unfortunately, the need to know the exact sizes of the memory blocks makes any practical usage of memory pools with standard C++ allocator-based approach rather problematic since users often “hide” real properties of allocations which are made under the hood. For example: STL’s node-based containers like 'std::map' as well as other standard mechanisms like 'std::promise' or 'std::allocate_shared'. Being a company which focuses on real-time safety-critical applications, we still see a significant value in keeping compatibility with the standard allocator model as well as in following common conventions which are familiar to every C++ developer. This talk presents an approach which uses a combination of a memory allocator implementation which instruments the code, and an external LLVM-based tool which extracts the instrumentation information and generates static memory pool definitions, allowing the allocator to switch from the heap to a memory pool without any further changes to the code. The presentation will walk through a simplest possible implementation of this approach. --- Misha Shalem C++ Architect, Apex.AI C++ developer with 16+ years of experience. Currently holds position of C++ Architect at Apex.AI, Palo Alto, CA --- Streamed & Edited by Digital Medium Ltd - *-----* Register Now For CppCon 2022: *-----*
ROS World 2020: Autoware Parallel Session

ROS World 2020: Autoware Parallel Session

Autoware.Auto is an open-source autonomous driving stack built on ROS 2. This track aims at providing an overview of the capabilities to the ROS 2 community. Autonomous Valet Parking The Autoware Foundation has completed its first software demonstration with Autoware.Auto - Autonomous Valet Parking! But what is Autonomous Valet Parking and how did we achieve this milestone? How close are we to roaming the streets with a fully autonomous vehicle? What's next for the Foundation? Get the answers to the questions and more! Localization and State Estimation in Autoware.Auto This talk is about algorithms for localization and state estimation implemented in Autoware.Auto. It explains what localization and state estimation are, goes into details of the typical methods used to implement these concepts, as well as presents architecture decisions specific to the Autoware.Auto implementation. Object Detection and Controls in Autoware.Auto This talk gives a holistic overview over the 3D object detection stack available in Autoware.Auto. In addition it provides insight into the Model Predictive Controller and the Pure Pursuit Controller in Autoware.Auto. Behavioral and Motion Planning in Autoware.Auto This talk provides details about the architecture and algorithms for the planning module in Autoware.Auto. It explains how the behavior planner makes decisions from information provided by other modules in Autoware.Auto and how the motion planner plans the trajectory to a given goal. 200mph ROS - How Indy Autonomous Speeds Open Source University teams are exploring high speed autonomous driving via Indy Autonomous Challenge. We discuss technical challenges and how the open source community is rallying with autonomous driving stack of Autoware.Auto, ROS 2, OpenCV, Eclipse CycloneDDS with iceoryx and Zenoh V2X. #iac2021
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